Rodrigues, Josemar; Leite, José Galvão A note on Bayesian analysis in \(M/M/1\) queues derived from confidence intervals. (English) Zbl 0893.62019 Statistics 31, No. 1, 35-42 (1998). Summary: The purpose of this paper is to use the implied likelihood introduced by B. Efron [Biometrika 80, No. 1, 3-26 (1993; Zbl 0773.62021)] for making Bayesian inference about the traffic intensity \(\rho\) in an M/M/1 queue, without worrying about nuisance parameters. A comparison with the posterior distribution of \(\rho\) obtained by C. Armero and M. J. Bayarri [Tech. Rep. 10-92, Opt. Stat. Oper. Res., Univ. Valencia (1992)] is considered. Cited in 8 Documents MSC: 62F15 Bayesian inference 62M99 Inference from stochastic processes 62F25 Parametric tolerance and confidence regions Citations:Zbl 0773.62021 PDFBibTeX XMLCite \textit{J. Rodrigues} and \textit{J. G. Leite}, Statistics 31, No. 1, 35--42 (1998; Zbl 0893.62019) Full Text: DOI References: [1] Armero, C. and Bayarri, M. J. 1992a. ”Bayesian prediction in M/M/1 queues”. Department of Statistics and Operations Research, University of Valencia. Tech. Rep., 9–92 [2] Armero, C. and Bayarri, M. J. 1992b. ”Prior assessments for the prediction in queues”. Department of Statistics and Operations Research, University of Valencia. Tech. Rep., 10–92. [3] Cox D. R., J. R. Statist. Soc. 49 pp 1– (1987) [4] DOI: 10.1093/biomet/80.1.3 · Zbl 0773.62021 · doi:10.1093/biomet/80.1.3 [5] Gross D., Fundamentals of queueing theory (1985) · Zbl 0658.60122 [6] DOI: 10.1007/978-1-4612-1096-2 · doi:10.1007/978-1-4612-1096-2 [7] DOI: 10.1214/ss/1177011232 · Zbl 0955.62522 · doi:10.1214/ss/1177011232 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.